\begin{table}

\caption{\label{tab:mean_cv_performance_best_glmnet_sample_1}Mean cross-validation performance of best GLM-Net model in training split of first sample of labeled tweets.}
\centering
\fontsize{9}{11}\selectfont
\begin{tabular}[t]{lrrrrrrrrrr}
\toprule
\multicolumn{1}{c}{ } & \multicolumn{2}{c}{Balanced Accuracy} & \multicolumn{2}{c}{F1} & \multicolumn{2}{c}{Precision} & \multicolumn{2}{c}{Recall} & \multicolumn{2}{c}{Specificity} \\
\cmidrule(l{3pt}r{3pt}){2-3} \cmidrule(l{3pt}r{3pt}){4-5} \cmidrule(l{3pt}r{3pt}){6-7} \cmidrule(l{3pt}r{3pt}){8-9} \cmidrule(l{3pt}r{3pt}){10-11}
  & Mean & SD & Mean & SD & Mean & SD & Mean & SD & Mean & SD\\
\midrule
Mean & 0.603 & 0.020 & 0.391 & 0.035 & 0.395 & 0.035 & 0.389 & 0.031 & 0.816 & 0.012\\
``General'' & 0.584 & 0.041 & 0.300 & 0.077 & 0.326 & 0.087 & 0.283 & 0.077 & 0.886 & 0.023\\
``Specific'' & 0.627 & 0.047 & 0.337 & 0.083 & 0.343 & 0.095 & 0.339 & 0.092 & 0.915 & 0.021\\
``Unsure'' & 0.541 & 0.039 & 0.163 & 0.071 & 0.168 & 0.082 & 0.159 & 0.078 & 0.924 & 0.019\\
``No'' & 0.657 & 0.030 & 0.759 & 0.027 & 0.744 & 0.033 & 0.776 & 0.034 & 0.539 & 0.051\\
\bottomrule
\end{tabular}
\end{table}
